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Lecturers:Tim DalyPdraig McGuigan
Kimmitt Sayers
DESIGN: BUILDING ENERGYAND
ENVIRONMENTAL MANAGEMENTAND
CONTROL SYSTEM
4th Year Sustainable Design Practical Final Report
Candidate: Chris Pullen
Student Number: D00131950
Course Code: DK_ESUSD_8 Y4
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DESIGN: BUILDING ENERGYAND
ENVIRONMENTAL MANAGEMENTAND CONTROLSYSTEM4th Year Sustainable Design Practical Final Report
FOREWORD
This document and the content herein have been compiled from
research and practical works carried out by Chris Pullen at Dundalk
Regional Technical Institute and at various select Industrial premises
within Ireland. This project has been submitted for assessment and
consideration for credit against an Honours Engineering Degree in
Sustainable Design.
This project was extremely large in scope from the perspective of
design and technical difficulty. It was not possible to completely
design, test and deploy the complete system concept within a
timescale of only 2 semesters due to the limited resources available.
However the aim of this project was to implement some of the more
novel and exciting features of the system and thereby prove the
concept is valid and technically sound. A Bonus target was set to
actualise the project into a saleable product and to validate the
concept by proving that a real life market exists.
The product name will be referred to by the acronym BEEMS for
conciseness within this report (Building Energy Environmental
Management System). The concepts and features developed for
BEEMS were borne out of a great deal of application of thought and
experience in Industry by Chris Pullen. A consultation process was
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undertaken with Dundalk Institute of Technology Engineering faculty
and with several potential Industrial customers.
The consultation process had three important outcomes.
Firstly it confirmed through market and technical research that the
product concept was not only feasible but also marketable.
Secondly it highlighted and prioritised desirable features of the
product.
Thirdly it allowed the project Supervising Lecturers at Dundalk
Institute of Technology assess and guide the project from a broader
perspective, which included industry data and feedback, rather than
working with just theory and a prototype. This allowed for the setting
of clear project deliverable goals.
There are of course similar products on the market. Building
management systems (BMS) are very common indeed. However what
has been achieved with this product is a smarter, more cost effective
and more flexible system design than anything else currently available
on the market. The product delivers real cost savings and
environmental benefits. It is cost positive due to the short return on
investment (ROI) period. Therefore the BEEMs system is a triumph in
Sustainable Design and a validation of the ideology and thesis behindthe Sustainable Design Practical course at Dundalk Institute of
Technology.
STATEMENTOF ORIGINALITY
I hereby state that the works and concepts carried detailed in this
report are the work of Chris Pullen except where other sources arecredited. All site works and data collection has been undertaken by
Chris Pullen and not by any other party. This is an original work and
assistance from third parties is acknowledged at the end of the main
report.
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Signed:_____________________
Dated:______________________
Table of ContentsForeword .......................................................................................................... 2
Statement of Originality .................................................................................. 3
Table of Contents ............................................................................................. 4
........................................................................................................................ 8
Goals for Semester 2 ........................................................................................ 8
BEEMS Goals Category Diagram .................................................................... 11
Gantt Chart .................................................................................................... 12
IPPC licence Management through BEEMS ..................................................... 13
Instrument Panel with IPPC configured BEEMS at BOC Gases .....................15
Discharge to Sewer Monitoring point ........................................................ 15
Rain Gauge Installation .............................................................................. 15
Trend produced illustrating discharge flow ................................................. 15
Trend produced illustrating site rain fall..................................................... 16
Trend produced illustrating discharge flow and Rain fall............................17
The Mogden Formula ................................................................................... 18
Understanding the Mogden Formula ........................................................... 19
Calculating Rainfall footprint of BOC site .................................................... 21
IPPC BEEMS Business Potential................................................................. 21
Business Potential of IPPC Market in Republic of Ireland .............................22
Boiler / Energy Efficiency Management through BEEMS .................................24
Boiler Under BEEMS Control........................................................................ 25
BEEMS Algorithm for monitoring the Boiler efficiency .................................27
The following plots are MatLab modelling results of the algorithm.............32
MatLab Code for BEEMS System Boiler Control Algorithm ........................ 40
Photos - Trinity College Boiler rooms at the McNamara Building ................45
Photos from DkIT Boiler house - appraisal visit .......................................... 45
Costings for the implementation of Boiler Energy Efficiency Testing .........45
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Costings for the implementation of Fugitive gas with BEEMS system .........45
BEEMS system hardware costs (additional to either of above costs) ..........46
Tumble Dryer Energy Management through BEEMS ......................................47
Testing Strategy .......................................................................................... 48
Sensor and Transducer Installation ............................................................. 50
Handheld Instruments and BEEMS data logging .........................................50
Data Analysis - Graphical Display (Mimics) for live test data .....................50
Data Analysis - Trending modified-v-unmodified Vent Temperature ..........51
Data Analysis - Trending modified-v-unmodified Power Consumption ...... .54
Data Analysis - Spreadsheet Power difference in Vented Air Masses ........55
Sample Open Source Code for Dryer Testing ..............................................55
Fugitive Gas Detection and Alarming using BEEMS ........................................56
....................................................................................................................... 56
About Fugitive Gas ...................................................................................... 57
Fugitive Gas Monitoring Setup .................................................................... 59
Data Analysis Trending of Boiler House Gas Data ................................... 59
Flexible connectivity ................................................................................... 63
Mimics and user interface ........................................................................... 64
Example: Trending viewed through Internal Wed server mimic ...............65
Example: Live data viewed through internal web server mimic ...............65Example: Data retrieve embedded into internal web server interface .....66
Example: Channel Text listing viewed through internal web server ........66
Data trending .............................................................................................. 66
Alarm Logging and Reporting ...................................................................... 68
Example: Text Report file generated from logged data .......................... 68
Example: Alarm report automatically generated .....................................69
Legislation and funding .................................................................................. 70
Acknowledgements ........................................................................................ 70Appendix ........................................................................................................ 71
Sample Open Source Code for Dryer Testing .............................................71
DT85 Specification sheet ............................................................................. 73
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Summary of Semester 1 Report Content
The report compiled for Semester 1 covered the following topics:
Identify and establish a need for the product.
Industrial User Interviews (potential customers)
o BOC Gases
o Green Star , KTK facility
o Trinity College Dublin
Product Definition
o Product description
o Problem Statement
o Product Definition
Topology of BMS systems currently on the Market
The BEEMS system
o Overview of advantages of BEEMS over existing BMS
o BEEMS with conceptual Data over Mains (SCOM) system
o Diagrammatic Illustration of Key BEEMS applications
o Narrative of Key BEEMS applications
Project Objectives
o Degree Credit
o Sustainable Product Design
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Monitoring
Control
Reporting
o Cost Savings
Integration of OTS (off the shelf) Systems
Intelligent Application and Bespoke Algorithms
o Diagnostic and Analysis tools
Facilities (Estates) Management
Health and Safety Management
Personnel Management
Environmental Management
INvironment and Psychrometric Management
Energy Management
o Triple Bottom Line and Capital expenditure justification.
o Proposed Project Targets for Semester 2
Boiler / Energy Efficiency Management through
BEEMS
IPPC licence Management through BEEMS
Data over Mains (SCOM) communications with
BEEMS
GUI with BEEMS
Mimics and user interface
Data trending
Alarm Log / Reporting
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2-way SMS alarming and control through BEEMS
Outline of preliminary Boiler Control and management
Algorithms.
Outline of preliminary IPPC management Algorithms.
Acknowledgements.
Appendix of Research and support materials.
Glossary of terms.
GOALSFOR SEMESTER 2
At the beginning of semester 2, after consultation with Tim Daly and
Pdraig McGuigan, it was decided that the BEEMS project was in
danger of becoming undeliverable unless tight goals were defined and
focus on. The concept of the BEEMS system is extremely large in its
scope and the duration of semester 2 was quite limited. In order to set
these goals the following was done:
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Progress from semester 1 was reviewed.
Features required to market BEEMS to industry were considered.
Available time, equipment and facilities were assessed.
Progress from Semester 1 was reviewed and it was felt that the
marketing and research strongly suggested that the BEEMS concept
was viable as a product. The technologies involved were realistic and
the costings indicated the product would be attractive to consumers.
Critical analysis suggested that the project would have to be narrowed
significantly in order to successfully deliver the project on time and
functional.
Features required to market BEEMS to industry were considered. In
order to market the system the GUI would need to allow the customer
to trend historical data, to allow live viewing of parameters on screen
(preferably via an mimic), a reliable connection to the customers PC
must be available and alarming and reporting would be required. The
BEEMS system concept incorporates a suite of Pre-configured tools for
handling various building, energy and environmental management
tasks. At least 1 of these pre-configurations would have to be
completed and tested to deliver a product to market. Critical analysis
suggested that a Beta site would be required to prove the system
works as expected and to quantify a cost-benefit analysis for
marketing purposes.
Available time, equipment and facilities were assessed. It was decided
that the amount of work to be done in semester 2, in order to deliver a
completed project, was immense. The time scales were very tight so a
Gantt chart was produced and the tasks associated with each goal wasentered onto it. This allowed for the time management to be quantified
and some facets of the project (such as the SCOM system) proposed in
semester 1 were not pursued in semester 2. The Gantt chart was a
very useful tool to critically analyse the time management of this
project.
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The outcome of this process was the setting of the following goals:
1) IPPC licence Management through BEEMS
2) Boiler / Energy Efficiency Management through BEEMS
3) Fugitive Gas Detection and Alarming using BEEMS
4) Back End (User end) software interfacing to BEEMS
a. Mimics and user interface
b. Data trending
c. Alarm Log / Reporting
Some Bonus goals were set but would only be pursued after goals 1
4 above were achieved or significantly progressed.
5) To implement SMS alarm and control.
6) To realise the marketing and sale of the BEEMS system to
Industry.
Critical review of the project as outlined above also lead to the decision
not to proceed any further with the SCOM feature. The time and
resources required to deliver this feature were too great for the scope
of this project. The decision to sideline the SCOM feature was
important and was a result of the application of project planning and
management.
The Diagram shown on the following page (10) illustrates the goals set.
Each Boxed area of the BEEMS system represents a project goal. These
goals were laid out chronologically and broken down into sub tasks.
They were charted on a Gantt chart which is included on page 11 of
this report.
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BEEMS GOALS CATEGORYDIAGRAM
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GANTT CHART
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IPPC LICENCE MANAGEMENTTHROUGH BEEMS
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The Diagram on the previous page (12) shows the IPPC management
portion of the BEEMS system indicated in a grayed out box. An IPPC
licence is an integrated Pollution Prevention Control licence. These are
issues by the EPA (environmental protection Agency) to industrial site
operators where there is trade effluent deemed potentially hazardous
to the environment. The IPPC licence imposes strict monitoring and
control criteria on the licensee. Ongoing monitoring of effluent quality
and quantitive parameters is required to ensure that any exceedances
above or below limits set out in the IPPC licence are recorded and
reported.
IPPC licence holders must bear all costs for the implementation of the
IPPC licence. Apart from the Cap-ex costs of the monitoring equipment
they also face significant ongoing cost for the reporting and
maintenance of their system(s) to comply with their licence conditions.
As part of this project a configuration was developed for the BEEMS
system to minimize the human input required to implement the IPPC
licence. This is by itself an attractive feature from a cost saving point
of view. However an additional Algorithm was developed to quantify
the volume of rain fall ingress from the customers site footprint into
the fouls sewers. Rain water (or storm water) should be separated out
and directed to the storm water drains where it then is discharge to
rivers. When storm water ingress to foul sewers occurs the IPPC licence
holder must pay the local authority for the treatment of this rainfall asif it were trade effluent. The charge for this treatment is based on the
Mogden formula and is qualitive as well as quantitive based.
The BEEMS system allows the IPPC holder to prove to the EPA and/or
local authority that a quantified volume of the trade discharge is in fact
storm water. This is used to then reduce the treatment bill form the
local authority. It can also be used internally (within the IPPC licensed
company) to justify cap-ex budgets and to calculate the time to recoup
the costs of remedial civil works through reduced charges.
The IPPC configured BEEMS system was installed in BOC Gases on the
Naas road in Dublin. The configuration was localized to their particular
licence and Beta tested over the course of 2-3 months. Data was then
collected and analysed and reports generated for the customer to
supply to the EPA for their quarterly report. The volumes of rainfall and
the man hour savings were quantified. BOC were very happy with the
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results and the ROI period. They decided to purchase the system and
are now considering systems for Belfast and Cork. The following pages
show the Beta system in situ as well as the data analysis and the
calculations required for the discharge to sewer monitoring.
Instrument Panel with IPPC configured BEEMS at BOC Gases
Diagram removed;
Discharge to Sewer Monitoring point
Rain Gauge Installation
Gauge Mounted on Pole. Self emptying Tipping
Mechanism.
Trend produced illustrating discharge flow
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Trend produced illustrating site rain fall
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Trend produced illustrating discharge flow and Rain fall
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The Mogden Formula
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Factories and soon restaurants, pubs, farms and hotels will have toadhere to strict effluent discharge conditions as set out in an IPPClicence. If this effluent (waste water) is discharged to foul sewer it willultimately end up in a municipal sewage plant. Since the localauthority has to burden the cost of treating this effluent it recoups the
cost (plus a handsome profit) by levying charges per cubic metertreated. These charges are calculated based on a number ofparameters based on the quantity and quality of the effluent.
In order to accurately and repeatedly assess the charges to be leviedon the IPPC licence holder an industry standard formula called theMogden formula is employed.
The Mogden Formula is as follows:
C = R + V + Vb + (B x Ot/Os) + (S x St/Ss)
Where:
C = Total charge rate for disposal (Euro/cubic metre)
R = Unit cost for conveyance (Euro/cubic metre)
V = Unit cost for volumetric treatment (Euro/cubic metre)
Vb = Additional volume charge if there is no biological treatment
B = Unit cost for biological treatment (Euro/cubic metre)
Ot = COD of trade effluent (mg/l);
Os = COD settled sewage (mg/l)
S = Unit cost for sludge disposal (Euro/cubic metre)
St = Solids value trade effluent (mg/l);
Ss = Solids value* settled sewage (mg/l)
Understanding the Mogden Formula
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Waste water treatment costs are dependent on the local authorityrates as well as:
1) Volumes discharged
2) COD (Chemical Oxygen Demand)
3) Suspended Solids
4) BOD (Biological Oxygen Demand)
The treatment charges can be reduced by reducing any of theparameters in the Mogden formula. The BEEMS system reduced thesecosts by accurately quantifying the discharge volumes, quantifying theration of trade effluent versus storm water and on larger systemscalculating the costs at the licence holders site itself by measuring the
water quality parameters using inline probes.
The Mogden formula also allows for the charging for treatment forstorm water volumes (Vb) as well as trade effluent volumes (V). This isbecoming more and more common as local Authority want industry toremediate their storm water and foul water systems. This reduces thetreatment plant energy costs but saddles effluent discharge industrieswith the cost of the remedial civil works. However the BEEMS systemwill still significantly reduce the treatment costs as the tariff for Vb canbe as little as 20% of the tariff for V. Therefore the more storm waterthat can be proven to be a component of the total discharge volume,
the lower the treatment cost.
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Calculating Rainfall footprint of BOC site
Site footprint in square meters = 19,918 m3
Total Q1 rainfall in meters = 0.1253 m (from Rain gauge / BEEMS)
Total rainfall Discharged Q1 2011 = 2,496 m3 ( Total Rain x Footprint )
Estimated Annual Rainfall discharge to sewer = 10,000 m3
Estimated treatment cost for Rainfall discharge = 8,700 per annum
BEEMS system cost = 10,000 (once off Capital expenditure)
BEEMS maintenance cost = 1,100 per annum
Estimated Return on Investment Period = 18 months.
IPPC BEEMS Business Potential
ORDERS FOR SYSTEM ALREADY ACHIEVEDClient Description Order Value
BOC Gases IPPC Management system Dublin Plant ~ 10,000
[ Cork and Belfast sites under consideration]
Uisce Technology IPPC Management system - Trial site #1 ~ 6,000
Uisce Technology IPPC Management system - Trial site #1 ~
6,000
Uisce Technology IPPC Management system - Trial site #1 ~
6,000
Uisce Technology IPPC Management system - Trial site #1 ~
6,000
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[Up to 20 sites per year under consideration]
IFM IPPC Management system Galway Trial ~ 4,000
[ 3 Other Sites under consideration]
Total~
38,000
The figures above illustrate the IPPC configured BEEMS system sales as
of 20th April 2011. The difference in the cost for the systems is
accounted for as follows:
The BOC order was a complete turnkey supply, install and commission.
Hence the system cost is full anticipated retail cost.
The Uisce Technology orders are on a supply and final commissionbasis and the installation costs are borne by the customer. So there
are significant Labour and sundry parts savings compared to the BOC
sale. Also there is some quantity discount included as the customer
ordered 4 systems to assess.
The IFM sale is for supply only and all install and commission is at the
remit of the customer. Hence the relatively low price.
The differing technical support requirements of these orders will help
to highlight any technical issues with the product delivery.
Business Potential of IPPC Market in Republic of
IrelandEPA Licences granted in 2009: 54
EPA Licences granted in 2008: 40
EPA Licences granted in 2007: 51
EPA Licences granted in 2006: 34
EPA Licences granted in 2005: 27
EPA Licences granted in 2004: 31
EPA Licences granted in 2003: 38
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EPA Licences granted in 2002: 36
EPA Licences granted in 2001: 44
EPA Licences granted in 2000: 59
EPA Licences granted in 1999: 73
Total Number of IPPC licences listed on EPA.ie : 1025
County/City councils: 34
Borough councils: 5
Town councils: 75
Estimated Number of Discharge licences : 4000
10% Market share: 500 @ 5,000 = 2.5 million
[conservative]
20% Market share: 1,000 @ 7,000 = 7 million [optimistic]
Potential added value items (service & spares): = 375K - 1
million
Gross Profits: 40% (Product Sales), 55% (Value added Service
work).
These figures are current market value not per annum values. However
legislation changes will bring Restaurants, hotels, bars, farms and
many other industries under the IPPC umbrella making the figure
above realistic annual targets going forward.
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BOILER / ENERGY EFFICIENCY MANAGEMENTTHROUGH BEEMS
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The above diagram shows the Boiler / Energy Efficiency Management
feature of BEEMS as indicated by the grayed box which is the second
project goal.
Boiler Under BEEMS Control
Theory of operation
In Figure B1 above the Thermostat (1) is a temperature sensor,
normally a simple Bi-metallic strip, used for crude ON/OFF control of
the Boiler Burners. With the BEEMS system temperatures from several
rooms could be aggregated to determine the burner requirement. The
circulation Pump (11) could be replaced with a Variable speed drive
under the governance of the BEEMS system. This would allow for very
close matching of output to demand. It would also reduce overshoot ofthe system which would reduce Energy wastage. In order to reduce
costs and for ease of retrofitting existing Boiler systems the BEEMS
system could employ existing circulation pumps with the addition of a
proportional control valve. The BEEMS system could implement PID
control techniques on the control system to give a very smooth and
accurate environmental response.
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The Boiler Controls (2) would still operate the boiler but only in thecrude ON/OFF fashion originally designed for. The Controls (2) only job
is to switch the Burner (3) in and out as demand requires. This is done
by means of an integral solenoid valve and switching power through to
the Fuel Pump (4) (Note: if the fuel is gas then there is no fuel pump
but rather a spring closed valve known as a slam shut valve).
The Fuel Pump sucks oil through the filter (5) and delivers it under
moderate pressures to the burner nozzles which protrude into the Burn
Chamber (6). The nozzles need oil delivered at pressure so they can
aspirate (create a mist or spray) the oil which allows for efficient
mixing with the second combustant, i.e. Oxygen from air. This fuel/air
mix is ignited by either a spark or a pilot flame (depends on the fuel
and the system setup). During operation the combustion box gets
extremely hot. At this point the combustion thermal energy heats the
Heat Exchanger plates (10) (Also called the Boiler because this is
where the water in the system gets heated). The Hydronic system
passes water (or a thermal oil or a water glycol mix) through the heat
exchanger via a series of small gauge pipes that pass back and forth
through the heat exchanger block.
The heated water is pumped through the delivery side of the system
by means of a Circulation Pump (11). An Expansion Vessel (12) is
located on the hot feed flow line from the Boiler. This is a critical piece
of equipment. Since the system is a closed loop system and water
cannot be compressed, then , should the water get too hot it cannot
expand or compress. It would be trapped in the pipe work of fixed
dimensions until the heat energy in the water would overcome the
material strength of the weakest parts of the system. The result would
be a blow out with super hot water (steam) being ejected. This wouldbe very dangerous as well as costly in terms of damages. The
expansion vessel is filled with slightly compressed air (0.5 to 1 bar in a
domestic system would be typical). As the system heats up the water
can expand by further compressing the air in the expansion vessel.
Since the water cannot be compressed then the force of the air in the
vessel expanding is transferred hydraulically through the water. A
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Pressure relief valve close to the boiler will blow open relieving the
pressure in a controlled fashion should the system get too hot and
therefore the system pressure get too high.
BEEMS Algorithm for monitoring the Boiler efficiency
The concept behind the BEEMS system is to allow for intelligent
management of energy and environmental parameters within the
remit of its building management role. Therefore as part of this project
two features were explored that offer users real cost savings and
genuine improvement over current typical setups. These features are
not exhaustive. They are to demonstrate the potential of the product.
Time and resource limitations prevent further development of the
Boiler management at this time.
The first feature explored is the monitoring, in real time, of the boiler
efficiency. This allows users to then:
View trends (historical data)
Generate energy efficiency reports The data can be used as a
powerful tool to
o Improve the boilers usage
o Determine optimal service intervals
Generate Automatic alarms via various communications methods
(especially SMS)
Allow for restarts in trip situations remotely (especially by SMS)
Monitor and report TWA (Time Weighted Averages) Health and
safety requirements.
Monitor and report STEL (Short Term Exposure Levels) Health
and Safety requirements.
Automatically trigger a Slam shut valve on the gas supply line in
case of fugitive gas detection (gas leak)
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The algorithm developed for the energy efficiency is as
follows:
Energy out of Boiler = [(Tout Tin) x Qout*] x Specific heat
capacity of hydronic fluid
*Qout = hydronic fluid mass flow = Volumetric flow x Specific gravity of
fluid.
Energy in to Boiler = Qin x Net Calorific value of fuel.
Energy out
Boiler Efficiency % = Energy in X 100%
As already stated the concept behind the BEEMS system is to allow forintelligent management of energy and environmental parameters
within the remit of its building management role. So the Algorithm
developed above is a very useful management tool for monitoring the
boiler operation. The next step is to develop another Algorithm for the
BEEM system to Control the boiler operation as efficiently and
smoothly as possible.
To summarise the exploring Figure B1 the BEEMS system could be
used to tightly and smoothly control the output of the boiler based on
temperature(s) in the INvironment. The thermostat would becomeonly a fall back control (becomes a failure scenario rather than the
norm in an ON/OFF control methodology). The addition of a
Proportional control valve on the Boiler output would allow for variable
flow rates and therefore variable heat delivery into the Invironment.
The following is the development of an Algorithm to control this
Variable heat delivery system.
Figure B2 illustrates the Algorithm in a block diagram fashion. The set
point is the desired temperature to be obtained from the heating
system. The controller is the BEEMS system ( or more accurately thealgorithm about to be developed here). The Valve is the proportional
control valve that can be used to throttle the Heat flow rates. The heat
Exchanger is the Boiler itself and would need to be tuned for each
Boiler by plugging in manufacturers data. Alternatively the Boiler
Efficiency algorithm previously developed could be used to achieve
this tuning. The last element in the model is the feedback component
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which is a temperature sensor (or sensors) placed in the Invironment
to be temperature controlled. The Controller will require PID
(proportional Integral and Derivative) functionality, the Valve will be
modelled by a first order lag plus gain, the Heat exchanger will be
modelled by two first order lags plus gain (which is really one second
order component broken into two first order factors i.e. one for each
temperature component in the heat exchanger), The temperature
feedback is also modelled by a first order lag and gain.
Now that a block diagram model for the control system has been
developed it is necessary to progress to a mathematical model. This is
done below in Figure B3. First the open loop response for the model is
determined. This was done using a software package called MatLab.
The Program used for the system model can be seen on page 31.
However first a discussion of the Model illustrated in Figures B3 & B4
(page 30) is required.
Under ideal circumstances the temperature out of the system (right
hand side) should be equal to the set point fed into the system (on the
left hand side). However disturbances in the system will lead to errors
and therefore drift between the desired and the actual temperature.
To overcome this the controller will act to position the valve until theheat released from the heat exchanger is such that the output
temperature is equal to the set point (actual = Desired). Therefore the
error will be Zero, The temperature sensor provides the signal being
fed back for comparison with the set point and the control will act
proportionally to the size of the error between the actual and the
desired temperatures.
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The following mathematical operators have been used:
s=tf('s'); S is a Laplace Transfer function so we dont need to do any
calculus
Kp= PID proportional gain constant
Kd= PID derivative gain constant
Ki=PID integral gain constant
Kv=Valve Gain constant
Kh=Heat Exchanger gain constant
Kt=Temperature transducer (feedback loop) gain constant
Tv=Tau constant for Valve
Th1=Tau constant 1 for Heat Exchanger 1st order
Th2=Tau constant 2 for Heat Exchanger 1st order
Note: (Th1xTh2) gives a 2nd order
Tt=Tau constant for temperature transducer.
T=Sample period
A = Kp*(1+Kd*s+Ki/s) = PID response model
V = Kv/((Tv*s)+1 = Valve response model
HE= Kh/(((Th1*s)+1)*((Th2*s)+1)) = Heat Exchange response model
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TT=Kt/(Tt*s+1) = Temperature feedback sensor response
model
OLT=(V*HE*TT) = the Open loop transfer function to a step response
of the system
CLT=(Kp*V*HE*TT) = the Closed loop Transfer Function (PID excludedfor OLT not for CLT)
CLT=(Kp*V*HE)/(1+(Kp*V*HE*TT)) = Calculate Closed loop function
with feedback, P only.
CLT=((Kp*(1+Kd*s))*V*HE)/(1+((Kp*(1+Kd*s))*V*HE*TT)) = Add
Derivate gain to PID model.
CLT=((Kp*(1+Kd*s+(Ki/s)))*V*HE)/(1+((Kp*(1+Kd*s+(Ki/s)))*V*HE*TT))
= Add I gain to PID .
The following plots are MatLab modelling results of the
algorithm.
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Valve Model Transfer Response to Step input.
As expected the response is first order Lag + Gain in nature.
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Heat Exchanger Model Transfer Response to Step input.
As expected the response is second order Lag + Gain in nature.
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Temperature sensor Model Transfer Response to Step input.
As expected the response is 1st order Lag + Gain in nature.
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Open Loop Transfer Function Bode Plot of frequency to determine Gain
Margin for Controller.
36
At phase angle of -180 Deg the
frequency is 0.389 radians/second.
At 0.389 radians /second the Gain
margin is -13.9 dB.
=>13.9dB = 20 log (Gain) =13.9/20
= log (gain)
=>0.695 = log (gain)
Gain = 100.695
=> Gain = 4.955 (max gain) (above
this system unstable)
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Closed Loop Transfer Step Response with Gain Margin in unstable region.
37
The Transfer response is unstable in this plot. This
is because the Kp (Proportional gain) was set to 6
when we had calculated the gain margin to be4.955. SO this response is as expected and proves
our gain margin calculation.
Notice the Oscillation growth. This could be
destructive to equipment if implemented in the real
world. Our Algorithm for the BEEM system wont
do this because of our Mathematical
modelling/Simulations.
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Closed Loop Transfer Step Response with Gain Margin in stable region.
38
Gain of controller (P only gain) kept below gain margin
of 4.955 so the response is stable . However theovershoot is quite large at approx 25%. So we need to
add some Integral gain to slow the rise ramp up rate.
Overshoot has a cumulative tariff on energy costs so
we want to
Dampen the response to get a fast and smooth
response.
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Closed Loop Transfer Step Response with Integral (Ki) as well as
Proportional (Kp) Gain.
39
We added a small amount of Integral Gain but it
made little difference for this system.
This is expected because the Integration of a
step is approximately the same shape as the
plotted response anyway. We now need to see if
adding Derivative Gain (Kd) will
Help to dampen the system response. It should
as this type of gain has a Predictive nature.
Notice the steady state level has increased due
to the extra gain of the controller.
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Closed Loop Transfer Response with Proportional (Kp), Integral (Ki) and
Derivative (Kd) Gain.
MatLab Code for BEEMS System Boiler Control Algorithm
40
This is a beautiful response. Notice how smooth the
rise is and notice that we have virtually eliminated
overshoot. This algorithm would give a very energyefficient control on our BEEM system boiler control.
Also the steady state error is very flat so the valve
will not be hunting unnecessarily.
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s=tf('s'); % Setting S as a transfer function
Kp=9; %PID proportional gain constant
Kd=1; %PID derivative gain constant
Ki=1; %PID integral gain constant
Kv=1;%Valve Gain constant
Kh=10;%Heat Exchanger gain constant
Kt=0.1;%Temperature transducer (feedback loop) gain
constant
Tv=1.5; % Tau constant for Valve
Th1=5; % Tau constant 1 for Heat Exchanger 1st order
Th2=3;%Tau constant 2 for Heat Exchanger 1st order
(Th1*Th2) gives a 2nd order
Tt=2;%Tau constant for temperature transducer.
T=1; %Sample period%%
A=Kp*(1+Kd*s+Ki/s);
figure(1)
step(A);
%%
V=Kv/((Tv*s)+1);
figure(2)
step(V);
%%
HE=Kh/(((Th1*s)+1)*((Th2*s)+1));%
figure(3)
step(HE);
%%
TT=Kt/(Tt*s+1);
figure(4)
step(TT);
%%
OLT=(V*HE*TT);%Multiply out Open Loop Transfer Function
(PID excluded for OLTF)figure(5)
Step(OLT);
Bode(OLT)
%%
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Kp=0.978;
CLT=(Kp*V*HE*TT);%Multiply out Open Loop Transfer Function
(PID excluded for OLTF)
figure(13)
Step(CLT,100);
%%
% Controller gain was calculated at max 4.954 before
exceeding the
% -180Degree gain margin and the loop becoming unstable
Kp=1.09;
CLT=(Kp*V*HE)/(1+(Kp*V*HE*TT));
%CLT=feedback(OLT,TT);
%B1 = OLT;
%B2 = TT;%B3 =feedback(OLT,TT);
figure(7)
step(CLT,200);
%%
OLT=(V*HE*TT);%Multiply out Open Loop Transfer Function
(PID excluded for OLTF)
figure(9)
step(OLT);
rlocus(OLT)
%%
Kp=0.978;
Kd=0.5;
CLT=((Kp*(1+Kd*s))*V*HE*TT);%Multiply out Open Loop
Transfer Function (PID excluded for OLTF)
figure(12)
Step(CLT);
%%rlocus(CLT)
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%%
Kp=1.13;
Kd=0.5;
CLT=((Kp*(1+Kd*s))*V*HE)/(1+((Kp*(1+Kd*s))*V*HE*TT));
%CLT=((Kp*(1+Kd*s))*V*HE*TT);%Multiply out Open Loop
Transfer Function (PID excluded for OLTF)
figure(13)
Step(CLT,200);
%rlocus(CLT)
%%
Kp=1.2;
Kd=3.5;
Ki=0.1;
CLT=((Kp*(1+Kd*s+(Ki/s)))*V*HE)/(1+((Kp*(1+Kd*s+(Ki/s)))*V*HE*TT));
%CLT=((Kp*(1+Kd*s))*V*HE*TT);%Multiply out Open Loop
Transfer Function (PID excluded for OLTF)
figure(261)
Step(CLT,200);
%rlocus(CLT)
%END OF SYSTEM MODEL
As can been seen from the modelling plots the Algorithm for the
boiler control using the BEEMS system works very well in Mathematical
simulation. Unfortunately the time and cost constraints of this project
do not allow the testing in practice of this algorithm. For this reason a
lot of time was spent getting it fully modelled and simulated in
MatLab. This demonstrates the concept principle.
It was planned that Dundalk Institute of Technology would allow access
to boiler plant to perform a deployment of the BEEMS system for
energy efficiency testing. Written requests and proposals weresubmitted to the Estates department at the college and Tim Daly and
Dr. Dan OBrien assisted greatly in negotiating access to a plant room.
Initially it did not appear that this access would be granted. A
contingency plan was formulated whereby TCD (Trinity College Dublin)
had agreed to allow the project to be tested on one of their boiler
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systems. The installation requirements were assessed during the
interview process of with industry in semester 1. The plan was to fit 2
clamp on thermocouples and clamp on time of flight ultrasonic flow
meters (1 for the hot water and 1 for the incoming oil or gas). This
would allow a full test the boiler using the BEEMS system and the
energy efficiency algorithm developed earlier (see page 26).
In order to proceed further with these works the following
documentation would need to be put in place:
Insurances
Training certification
Health and Safety statements
Risk Assessment for works
Method statement for works
The pictures on Page 44 illustrate the plant room offered by TCD.
However Dundalk Institute of Technology did grant access to their
plant and the TCD site was not required. The pictures on Page 45
illustrate the plant room at DkIT which was assessed for these works.
Tim Daly (Engineering faculty) and Christian Maas (Estates
Department) assisted in providing access to and use of the Boiler plant
room. Christian was particularly helpful in determining the best way to
approach the deployment of any equipment required.
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Photos - Trinity College Boiler rooms at the McNamara Building
Photos from DkIT Boiler house - appraisal visit
Tim Daly and Christian Maas alongside Duty and Standby Gas Boilers
Incoming gas flow meter (left) and Boiler output hot feed lines and pumps
(right)
Proposed flow meter installation point and electrical control cabinet.
Costings for the implementation of Boiler Energy Efficiency
Testing
1 x DN100 Mag5100W flow meter sensor 1,700 + VAT
1 x Mag5000 Converter for flow meter 1,200 + VAT
1 x Mag5000 remote mounting bracket and PCB 220 + VAT
1 x Special fitting fabrication 500 + VAT
Sum Fitter to fit Flow meter and remove later 800 +
VAT(estimated)
2 x PT100 clamp on temperature sensors (4-20mA) 300 + VAT
Sum Cable , cable tray, Thermal cladding 100 + VAT
Subtotal-1 4,820 + VAT
Costings for the implementation of Fu gitive gas with BEEMS
system
1 x Special cable for Gas meter 100 + VAT
(estimated)
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1 x OLCT10 CH4 gas sensor 200 + VAT
1 x CEX300 CO2 gas sensor 400 + VAT
1 x CEX300-IR O2 gas sensor 800 + VAT
1 x slam shut valve (not required will use relay to mimic)
Sum Calibration gases (N2, Clean Air, CH4, CO2) 450 + VAT
Subtotal-2 1,950 + VAT
BEEMS system hardware costs (additional to either of above
costs)
1 x DT85 Data logging module 2,000 + VAT
1 x UPS of system 50 + VAT
1 x GSM modem for SMS alarms and control 150 + VAT
12 x relays for Plant pump monitoring/trip reset 240 + VAT
Subtotal-3 2,440 + VAT
The cost of implementing the boiler energy efficiency testing proved to
be too expensive for the budget available. This was most unfortunate
but unavoidable. However it was decided to still use the accessgranted to the Boiler plant room to carry out a deployment of the
BEEMS system to prove the concept of fugitive gas monitoring and
control. This will be discussed in the Fugitive Gas section (See Page
58).
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TUMBLE DRYER ENERGY MANAGEMENTTHROUGH BEEMS
Since the Boiler energy efficiency was too expensive to implement it
was decided to find an alternative application to demonstrate the
concept of using BEEMS for energy management purposes. Another
project running at DkIT was an improved Tumble Dryer system that
was more energy efficient than the standard model. Noel Rooney the
student carrying out this work is a Mechanical Engineering student and
was in need of assistance in collecting data on temperature, relative
humidity , air flow and electrical energy in order to profile and compare
the modified and unmodified tumble dryers. It is not within the remit of
this report to detail the engineering differences between these 2
dryers, rather this report will detail the testing performed by BEEMS.
Below is a picture of 2 identical tumble dryers manufactured by Creda.
The machine on the left has been modified by Noel Rooney in order to
achieve energy efficiency. The machine on the right is an unmodified
dryer as built by Creda.
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Testing Strategy
In order to profile and compare the two machines each dryer was fitted
with identical sets of sensors. BEEMS was configured to record data
measurements from all measuring points at the following intervals:
1 second (mimic update -no recording), 10 seconds, 1 minute and 10minutes.
48
Diagram Copyright of Chris Pullen
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The customer requested the following items to be logged:
Parameter Sensor
Ambient air intake temperature PT100
Drum temperature PT100
Vent temperature (exhaust air) PT100
Relative humidity Handheld meter
Air velocity Handheld anemometer
Phase voltage Voltage transducer
Phase current Current transducer
Time and date Data points stamped
Clothes weight pre dry Weighing scales
Clothes weight post dry Weighing scales
Vent pipe CSA Vernier calipers
Derived Parameter Sensor
Total Air Volume Vent CSA x Air Velocity x
Time
Total Electrical Power Voltage x Current x Time
Kg CO2 reduction Kwh usage x CO2 Kg/kwh
Energy cost Kwh usage x cost/kwh
Both Machines were data logged in parallel meaning all of the above
parameters were required for EACH machine.
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Sensor and Transducer Installation
Handheld Instruments and BEEMS data logging
The picture above illustrate the handheld instruments used during the
testing. Budget restrictions prevented the use of sensors, with signaloutputs suitable for data logging, for every parameter. Where
handheld instruments were used readings were taken every minute
and entered manual into the BEEMS system where they were recorded
with the other parameters. This was achieved by setting a list control
variable values, representing the manually measured parameters, to
the measured reading. The open source code in the DT85 Data-Taker
data logger which was used to form the data acquisition basis of the
BEEMS system allowed the CV values to be altered over a USB
connection from a PC or Laptop. The software was written to record CVvalues labelled as the parameters measured with handheld
instruments.
Data Analysis - Graphical Display (Mimics) for live test data
50
The Void, Drum and Vent Temperatures
were monitored using PT100 sensors as
shown in the pictures.
The phase voltage and phase currents
were monitored using voltage and
current transducers as shown in the
pictures.
Both machines were fitted withindependent equipment simultaneously.
Each Machine needed to be owered
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The above illustration is a screenshot of the mimics produced to allow
the customer (in this case a student) to view graphically their process
in real time. The mimics are a little crude but are more than sufficient
to demonstrate the feature of real time mimics being used in
conjunction with the BEEMS system. Improvements on the graphics
quality is simply a matter of purchasing a professional quality drawingpackage. Any bmp or jpeg image is compatible with the BEEMS
system software.
Up to 256 icon images can be embedded into a mimics screen. The
numerical values representing data channels are superimposed on top
of the background or icon image. These overlays are basically channel
number tags to tell the BEEMS software which channels value is to be
printed to the screen, what font colour and size, what the name of the
connection is (e.g. USB port 2) and how frequently to update the
screen print.
Data Analysis - Trending modified-v-unmodified Vent
Temperature
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The data trending on page 54 was produced from the data that was
collected from the testing of the Tumble Dryers. The customer was
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very happy with the trending as it greatly simplified the analysis of the
testing. This trend is just a sample of many trends produced for the
customer. The trending was an important item to deliver on as it is
kernel to the concept of using BEEMS as a tool for management and
analysis.
Similarly the customer feedback on the mimic screen was excellent as
it allowed the customer to assess the energy efficiency testing in real
time and to ensure that all monitoring equipment was working without
having to wait until the test was completed.
The next step was to demonstrate how the collected data could be
used to analyse the energy efficiency of the tumble dryers. In order to
do this a trend of the electrical power was produced to compare and
contrast the energy usage of both dryers over the testing period. Again
here the customer was extremely happy with the results. As can beseen from the trend on page 56 the BEEMS system data verified that
the energy inputted into both machines was almost identical at 2.40
Kwh for the unmodified machine and 2.42 Kwh for the modified
machine.
So the energy into both machines has been quantified. The clothes
inside were carefully matched to be the same material and weight.
They had the same water content since the loads came from the same
wash and spin. The next step to verify that the customer has identified
how to save energy from the tumble drying is to compare the energiesvented in the exhaust air. In other words how much electrical energy
can be removed from the modified machine drying cycle and still
maintain the heat levels in the vented air which is present in the
unmodified machine (this difference being energy which was
previously being lost through inefficiency).
The illustration on page 57 is a screen shot of the BEEMS data being
analysed in a spread sheet format. The vented hot air energy
difference between the modified and unmodified machines is shown to
be 9.5% higher on the modified machine. This proves that the
customers modifications have verified that energy savings are possible
with the Creda tumble dryer. The customer can reduce the heating
element size or duty cycle in order to reduce the vented hot air energy
by 9.5% without reducing the drying effectiveness of the machine. It
was highlighted to the customer that the 9.5% energy saving which
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appears to be possible should actually be in the region of 12% as the
efficiency of inductive heating coils would be no better than 80%.
Therefore reducing the energy into these heaters would yield an
additional saving. The customer feedback from this portion of the
project was very positive and it also allowed for the BEEMS system to
be demonstrated to the project supervising Lecturers.
Data Analysis - Trending modified-v-unmodified Power
Consumption
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Data Analysis - Spreadsheet Power difference in Vented Air
Masses
Sample Open Source Code for Dryer Testing
The software configuration for the BEEMS system is done via
augmenting pre-existing code with open source code. The code for the
IPPC licence configuration is not being published here as it is of
significant commercial value. However the code for the tumbler dryer
energy testing that has been outlined in this report is available in theappendix at the back of this report (see page 73). The code would bulk
out the main body of the report unnecessarily. So it was felt the best
place to include the code is in the appendix. The MatLab code was
included in the reports main body because it was felt that the
significant time spent on MatLab should be highlighted.
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FUGITIVE GAS DETECTIONAND ALARMINGUSING BEEMS
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The above diagram shows the Fugitive Gas Management feature of
BEEMS as indicated by the grayed box which is the third project goal.
About Fugitive Gas
Having obtained clearance to use one of the DkIT boiler rooms for
BEEMS testing it was a disappointment when the Boiler Efficiency goal
was found to be too expensive to implement. It was decided to instead
use the boiler room for Fugitive gas emissions monitoring. Ideally it
would be desirable to measure a suite of gases and use the BEEMS
system to interpret the readings obtained to satisfy multiple
monitoring requirements.
CH4 (Methane) is a hydrocarbon gas burned as a fuel by boilers. The
CH4 gas can escape and become a health hazard so CH4 monitoring isimportant from a health perspective as well as an explosion risk
perspective. Although CH4 is colourless it does have an added odoriser
for safety. However the nature of Hydrocarbon gas is that they act to
anaesthetise the olfactory receptors in the nose even at very low
concentrations. So the smell is only an effective indication of the
presents of gas for a short initial period. CH4 is lighter than air and
detection equipment should be suitably located at a high level within a
confined space. Note: other hydrocarbon fuels commercially available
such ad propane, butane or LPG (a mix of propane and butane) are
heavier than air so detectors should be located no more than 0.3mabove floor level.
CO2 (Carbon Dioxide) is a by-product of the combustion process for all
hydrocarbon fuels. CO2 is an asphyxiant and therefore is hazardous.
CO2 is colourless and odourless. It is heavier than air and detection
equipment should be suitably located at close to the lowest floor level
in a confined space.
CO (Carbon monoxide) is another by-product of the combustion of
hydrocarbon fuels. The presents of CO can be particular problematicwith badly services burners or badly ventilated flues. CO is colourless
and odourless. It is highly toxic even at low concentrations as it bonds
in a similar fashion to the haemoglobin in blood (causing the blood to
become cherry red in colour). CO is slightly lighter than air so detectors
should be mounted no higher than 4 foot from the floor (or lower
where children are expected to be present).
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O2 (Oxygen) is the second fuel required for the combustion ofhydrocarbon fuels (or almost all fuels for that matter). O2 enrichment
is a health hazard but by far the most common hazard with O2 is
depletion. O2 levels below 17% can cause death. Even below 19% the
body can become light headed and clumsy. This is itself a danger. O2
is colourless and odourless. It is slightly heavier than air. O2 detection
equipment is often used for 3rd party detection. The idea behind this is
that a drop in the O2 concentration in air is an indication of one of the
following: combustion or oxidation are using up the O2 present in the
air, or a 3rd
party gas is present in higher than natural concentrationsand is occupying some of the volume in air normally taken up by O2.
O2 detectors are therefore located mid height in a confined space.
The monitoring of fugitive gas in the DkIT using a BEEMS system could
be employed to do any or all of the following:
Detect fugitive CH4 [Health and explosive risk]
Detect elevated CO2 levels [Health risk]
Detect elevated CO levels [Health risk]
Detect depleted levels of O2 [Health risk and possibly indication
ofother gas]
Monitoring if boiler burn becomes dirty
Monitor if ventilation to burner becomes obstructed
Monitor if ventilation of flue becomes obstructed
Monitor for TWA (Time Weighted Average) exposure levels forpersonnel
Monitor for STEL (Short Term Exposure Level) exposure levels for
personnel
Automatically activate slam shut vale on leak detection or limit
exceedance
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Automatically generate Alarms on leak detection or limit
exceedance
Report back live levels to personnel before confined space entry
Fugitive Gas Monitoring Setup
The photos above show the Fugitive gas detector fitted in the DkIT
boiler room. A CH4 detector was installed close to the ceiling. An O2
detector was fitted at mid height in the room. A CO2 detector was
fitted close to floor level and the DT85 was fitted in the Electrical
control panel .
Each detector was calibrated using bottles calibration gases applied at
a flow rate of 0.5 litres per minute (as per manufacturers
recommendations). The CH4 ,CO2 and CO detectors were Zero set
using a pure Nitrogen (N2) gas. The CH4 was spanned using a 2.5%
CH4 by Volume gas. The CO2 was spanned using a 2% CO2 by volume
gas and the O2 was spanned using Analytical Air which has 20.9% O2
by volume.
Each sensor had a 4-20mA output and the DT85 was calibrated against
these current loop signals while the zero and span gases were ON(being applied to the detectors). Therefore the whole system was
calibrated to industry standards using traceable gas.
Data Analysis Trending of Boiler House Gas Data
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The trend illustrated on the last page (62) shows the behaviour of the
CH4, CO2 and O2 gas in the boiler house during the deployment of the
BEEMS system. It is unremarkable since the boilers in this plant room
are very well serviced. Only trace amounts of CH4 is present (see blue
trend line) and a saw tooth pattern is discernable from the ON/OFF
operation of the boilers.
The CO2 trace (the red line) shows the natural background levels of
CO2 (approx 400ppm) found in Air but with the CO2 produced by the
boiler combustion superimposed. The CO2 trend tracks the CH4 trend
since the extra CO2 is produced as a function of the operation of the
Boilers and any trace CH4 is also a function of the boiler operation.
The O2 trace (black line) shows 2 interesting trends. Firstly the Diurnal
variation can be seen. The Diurnal variation is a natural variation in
Oxygen levels found in Air between Day and night due tophotosynthesis processes of plants and marine algae. The second
trend that can be seen is again the boiler operation superimposed.
Although the saw tooth feature of the CH4 trend cannot be seen there
is a definite correlation between the average CH4 levels and the
variation in the natural O2 profile.
Although the data collected by the BEEMS system does not have
include any risk events the capability of the system to accurately
and precisely track the gas mix within the confined space has been
very well demonstrated.
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Back End (User end) software interfacing to
BEEMS
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The above diagram shows the Backend Software features of BEEMS as
indicated by the grayed box which is the fourth project goal.
The Backend software features required for the BEEMS system need to
include at a minimum the following:
Flexible connectivity
Mimics and user interface
Data trending
Alarm Log / Reporting
Flexible connectivity
The BEEMS system has an extremely flexible communications
platform. Users can mix and match any of the following ways:
Ethernet 10BaseT (10Mbps)
o (TCP/IP protocol)
o Web Server
o FTP Server
o FTP Client
USB (ASCII Protocol)
Serial interfaces:
o ModBus
o RS232
o RS422m
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o RS485
o CAN Bus
Local LCD display and panel buttons
Front paned USB memory stick port
Mimics and user interface
The BEEMS system mimic capability was demonstrated within the
tumble dryer testing process. The capacity of the BEEMS system is 1-
900 channels per DT85 based hub. Larger systems are possible and
DT85 hubs could be made swap information using ModBus. However
there is a limitation with the User interface software if the system size
exceeds 1 hub max capacity of 900 channels. In such a case the user
would configure the software to send all the channel data to a
database and then retrieve the data required from that database. This
allows the system to be virtually limitless with regard to the number of
channels. However it would cause the system to become more
complicated as an extra data management layer is added. The
response time of the Backend software would also be adversely
effected.
The user has a choice of presenting data in a mimic screen, a
spreadsheet and a trending screen (graphing) as we have seen
already. There are also further choices of a text screen (a scratch pad
with updating text) and a web browser interface (the web server in
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built into the DT85 but must be configured to suit the BEEMS
application).
Example: Trending viewed through Internal Wed server mimic
Example: Live data viewed through internal web server mimic
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Example: Data retrieve embedded into internal web server interface
Example: Channel Text listing viewed through internal web server
Data trending
Historical data trending has been demonstrated throughout this report.
Trending was produced for the IPPC licence BEEMS configuration, for
the Energy efficiency BEEMS configuration and again for the Fugitive
gas BEEMS configuration. The feature is well tested and proven. Live
data trending is utilized as easily as historical trending. The only
difference is the user selects a channel on a live connection rather
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than one from a data file or database. Below are thumbnail views of
trends used throughout this report.
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Alarm Logging and Reporting
Alarm logging and report generation are crucial features to ensuring
the BEEMS system is commercially successful. In order to market the
system as cost effective there must be a significant and quantifiablecost benefit. The more a system such as BEEMS can automate
reporting and data management the lower the labour cost is to the end
user.
IPPC licensing is a very good case in point. The licence holder is legally
bound to adhere to strict reporting formats and time scales as
stipulated in the licence. IPPC licence holders must report an
exceedance to the EPA within 48 hours of an occurrence. They must
also generate reports based on hourly, daily, monthly, quarterly and
annual limits. The BEEMS system can be configured to Alarm onhigh , low or complex limits for any parameter.
These alarms are stored in an alarm log database and manual or
automated reports can be generated. The user can tailor these reports
to suit the EPA requirements by simply modifying the parameters and
time scale of interest. The reduction in resources and costs required to
meet reporting requirements are an attractive selling point and
demonstrate again the sustainability benefits of this product.
The EPA or licensing Authority can also save resources throughreduced audit requirements, since the data management is a more
secure format than manual data collection and manipulation. It is also
possible for BEEMS to publish reports and data directly to a static IP
address by means of TCP/IP push. This means that anyone interested
in the data or reports need no special softwares. Anyone with a web
browser and possibly a password can view the information.
With industry heading more and more down the route of implementing
Cloud computing topologies the BEEMS system is well placed to
compete in the market place.
Example: Text Report file generated from logged data
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Example: Alarm report automatically generated
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LEGISLATIONANDFUNDING
Systems such as BEEMS will be mandatory in the near future. Alreadythe European Parliament Directive 2002/91/EC imposes obligations on
member states to legislate for new buildings to meet minimum Energy
standards. Downward pressures in Europe and Ireland will lead to this
obligation being levies on all commercial and eventually domestic
buildings.
The SEAI Energy Efficiency Retrofit Fund (EERF) allows commercial
and academic facilities to apply for grant aid to carry out remedial
works to make buildings more energy efficient and sustainable. In
some cases up to 80% of the costs can be recouped. These documentsare included in the Appendix (See Page 85 Section 5).
ACKNOWLEDGEMENTS
DKIT Lecturers / Staff:
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Tim Daly,
Pdraig McGuigan,
Kimmitt Sayers,
Dr. Dan OBrien
Christian Maas
Dr. Eoin Clancy
Mark Clarke
James Mulvany
DKIT Students
Noel Rooney
Industry Contacts
Edward Keating Uisce Technology
Stephen Moran BOC Gases
Aidan Corrigan IFM
John Robinson Trinity College Dublin
Michael Bergin Green Star EnvironmentalTom Butterly Dublin City Council Heating Department
I would also like to thank my family for their patience and support
during my studies at Dundalk Institute of Technology. I also want to
thank Omni Instruments for financing the project.
APPENDIX
Sample Open Source Code for Dryer Testing'JOB=JOB1'COMPILED=2011/04/04 00:24:41'TYPE=DT85DT=\d
6*PT385("M Void",W,=6CV)56CV("M Void",=56CV)=6CV-1.227*PT385("C HE1 Pre",W,=7CV)57CV("C HE1 Pre",=57CV)=7CV-0.97
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BEGIN"JOB1"CATTN'Spans and polynomial declarationsS1=5.62,10.52,1.396,2.145"Amps"S2=0,238,7,14.2"VAC"S3=5.69,10.63,1.375,2.175"Amps"S4=0,238,0.49,14.24"VAC"'Thermistor declarations
'Switches declarations'Parameter declarations'Global declarationsRS1S'schedule definitionRA"LIVE"("B:",ALARMS:OV:100KB,DATA:OV:1MB)5SLOGOFFA GA1*PT385("U Drum",W,=1CV)51CV("U Drum DegC",=51CV)=1CV-1.182*PT385("U Vent",W,=2CV)52CV("U Vent DegC",=52CV)=2CV-1.683*PT385("U Void",W,=3CV)53CV("U Void Deg C",=53CV)=3CV-1.134*PT385("M Drum",W,=4CV)54CV("M Drum",=54CV)=4CV-1.465*PT385("M Vent",W,=5CV)55CV("M Vent",=55CV)=5CV-0.97
8*PT385("C HE2 Pre",W,=8CV)58CV("C HE2 Pre",=58CV)=8CV-1.219*PT385("C HE2 Post",W,=9CV)59CV("C HE2 Post",=59CV)=9CV-1.0610*PT385("C HE1 Top",W,=10CV)60CV("C HE1 Top",=60CV)=10CV-1.0811*PT385("C HE2 Top",W,=11CV)61CV("C HE2 Top",=61CV)=11CV-1.01
12*PT385("C Vent",W,=12CV)62CV("C Vent",=62CV)=12CV-1.2415*PT385("Room Air",W,=30CV)30CV("Room Air DegC",=31CV)=30CV-1.3513HV(S1,"U Amps ",W,=13CV)ALARMR(13CV("U Amp Zero")
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51CV("U Drum DegC")52CV("U Vent DegC")53CV("U Void Deg C")54CV("M Drum")55CV("M Vent")56CV("M Void")57CV("C HE1 Pre")58CV("C HE2 Pre")
59CV("C HE2 Post")60CV("C HE1 Top")61CV("C HE2 Top")30CV("Room Air DegC")15CV("U Power Kw")35CV("U Power Kwh")18CV("M Power Kw")36CV("M Power KwH")13CV("U Amps ave",AV)14CV("U VAC ave",AV)70CV("M Amps ave",AV)17CV("M VAC ave",AV)90CV("U RH % man")91CV("U VEL M/S man")92CV("M RH % man")93CV("M VEL M/S man")END'end of program file
DT85 Specification sheet
Full data specification downloadable at:
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http://www.datataker.com/Library/Product_Data_Sheets_TS/TS-0067-E1%20-
%20DT85.pdf
Terms
BATNEC Best Available Technology Not incurring Excessive
Cost
BEEMS Building Energy and Environmental Management
System
BMS Building Management System
BOD Biological Oxygen Demand
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BTU British Thermal Unit
Cap-Ex Capital Expenditure
CH4 Methane
CO Chemical symbol for Carbon Monoxide gas
COD Chemical Oxygen Demand
CO2 Chemical symbol for Carbon Dioxide gas
CSA Cross Sectional Area
CSR Corporate Social Responsibility
CV Channel Variable
DkIT Dundalk Institute of Technology
EC European Community
EPA Environmental Protection Agency
Ethernet Ether (from Greek meaning Anywhere) Network
EU European Union
Flume An open artificial channel used for flow gauging
FSK Frequency Shift Keying
Fugitive Gas Pollutant released to air from equipment and
plant leaks
Glycol An alcohol of 2 Hydroxyl groups, used for heattransfer.
GSM Global System for Mobile communications
GUI Graphical User Interface
HMI Human Machine Interface
HVAC Heating, Ventilation and Air Conditioning
Hydronic Term to describe the transfer of heat by water
Internet Interconnected Network
INvironment Indoor environment, term defined for this project
I/O Input /Output, used to describe field interface
devices
IPPC Integrated Pollution Prevention Control
Klaxon A loud siren used for raising alarms.
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Kw Kilowatt , unit of electrical power
Magmeter Magnetic flow Meter
MatLab Software Package for Mathematical Modelling
mg/l milligram per litre
Modbus Modicon communications Bus
NVM Non-Volatile Memory
OLE Object Linking and Embedding
OPC OLE for Process Control
Optoisolator Optically Isolated signal interface device
OTS Off The Shelf
O2 Chemical symbol for Oxygen gas
PC Personal Computer
PID Proportional Integral Derivate
PLC Programmable Logic Controller
Potable Water fit for human consumption
ProfiBus Process Field Bus
PSTN Public Switched Telephone Network
PT100 Platinum Resistance device with resistance of 100
@ 0CQin / Qout Flow in/Flow out, Q is notation representing a liquid
flow
RTU Remote Telemetry Unit
SCADA Supervisory Control And Data Acquisition
SCOM Superimposed Communications Over Mains Chris Pullen
SDF Secure Data Format
SEAI Sustainable Energy Authority of Ireland
Sewage Liquid and solid waste carried off in sewers or drains
Sewerage A system of sewers or drains to carry away sewage.
SHEQ Safety, Health, Environmental and Quality
SMS Short Message Service
Solenoid Magnetising coil powered by electric current.
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STEL Short Term Exposure Limit
TBL Triple Bottom Line, a term indicating the social,
environmental and financial cost as opposed to solely
a
financial cost (Bottom Line)
TCD Trinity College Dublin
TWL Time Weighted Average
UPS Uninterruptable Power Supply
USB Universal Serial Bus
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